Proceedings of the 2016 Workshop on Artificial Intelligence and Security Workshop (AISec 2016)

Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose applications have security-sensitive elements. However, while security researchers often utilize such techniques to address problems and AI/ML researchers develop techniques for Big Data analytics applications, neither community devotes much attention to the other. Within security research, AI/ML components are usually regarded as black-box solvers. Conversely, the learning community seldom considers the security/privacy implications entailed in the application of their algorithms when they are designing them. While these two communities generally focus on different directions, where these two fields do meet, interesting problems appear. Researchers working in this intersection have raised many novel questions for both communities and created a new branch of research known as secure learning. The AISec workshop has become the primary venue for this unique fusion of research. In recent years, there has been an increase of activity within the AISec/secure learning community. There are several reasons for this surge. Firstly, machine learning, data mining, and other artificial intelligence technologies play a key role in extracting knowledge, situational awareness, and security intelligence from Big Data. Secondly, companies like Google, Facebook, Amazon, and Splunk are increasingly exploring and deploying learning technologies to address Big Data problems for their customers.

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BibTeX @book{Freeman2016,author={Freeman, David and Mitrokotsa, Aikaterini and Sinha, Arunesh},title={Proceedings of the 2016 Workshop on Artificial Intelligence and Security Workshop (AISec 2016)},isbn={978-1-4503-4139-4},abstract={Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose applications have security-sensitive elements. However, while security researchers often utilize such techniques to address problems and AI/ML researchers develop techniques for Big Data analytics applications, neither community devotes much attention to the other. Within security research, AI/ML components are usually regarded as black-box solvers. Conversely, the learning community seldom considers the security/privacy implications entailed in the application of their algorithms when they are designing them. While these two communities generally focus on different directions, where these two fields do meet, interesting problems appear. Researchers working in this intersection have raised many novel questions for both communities and created a new branch of research known as secure learning. The AISec workshop has become the primary venue for this unique fusion of research. In recent years, there has been an increase of activity within the AISec/secure learning community. There are several reasons for this surge. Firstly, machine learning, data mining, and other artificial intelligence technologies play a key role in extracting knowledge, situational awareness, and security intelligence from Big Data. Secondly, companies like Google, Facebook, Amazon, and Splunk are increasingly exploring and deploying learning technologies to address Big Data problems for their customers.},publisher={ACM Press},place={New York, USA},year={2016},keywords={artificial intelligence, security, privacy, machine learning},}

RefWorks RT Book, WholeSR ElectronicID 245552A1 Freeman, DavidA1 Mitrokotsa, AikateriniA1 Sinha, AruneshT1 Proceedings of the 2016 Workshop on Artificial Intelligence and Security Workshop (AISec 2016)YR 2016SN 978-1-4503-4139-4AB Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose applications have security-sensitive elements. However, while security researchers often utilize such techniques to address problems and AI/ML researchers develop techniques for Big Data analytics applications, neither community devotes much attention to the other. Within security research, AI/ML components are usually regarded as black-box solvers. Conversely, the learning community seldom considers the security/privacy implications entailed in the application of their algorithms when they are designing them. While these two communities generally focus on different directions, where these two fields do meet, interesting problems appear. Researchers working in this intersection have raised many novel questions for both communities and created a new branch of research known as secure learning. The AISec workshop has become the primary venue for this unique fusion of research. In recent years, there has been an increase of activity within the AISec/secure learning community. There are several reasons for this surge. Firstly, machine learning, data mining, and other artificial intelligence technologies play a key role in extracting knowledge, situational awareness, and security intelligence from Big Data. Secondly, companies like Google, Facebook, Amazon, and Splunk are increasingly exploring and deploying learning technologies to address Big Data problems for their customers.PB ACM PressLA engLK http://dl.acm.org/citation.cfm?doid=2976749.2990479OL 30